Essay Available:
Pages:
8 pages/≈2200 words
Sources:
4 Sources
Style:
Other
Subject:
Mathematics & Economics
Type:
Essay
Language:
English (U.S.)
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MS Word
Date:
Total cost:
$ 34.56
Topic:
The Impact of Covid-19 on Disability to Work From 2019 Up to the Present
Essay Instructions:
The subject of our group's research is the impact of Covid-19 on disability to work from 2019 to the present. You need to make a ppt and write a presentation speech. The speech is used to explain the ppt (informal and colloquial) and Stata software is needed. This is the outline video link: https://www(dot)youtube(dot)com/watch?v=nUd445BKZoI
This is the Cleaning data link https://www(dot)youtube(dot)com/watch?v=nUd445BKZoI
Essay Sample Content Preview:
Empirical results
Model
Y=β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5
Y= 11.037 + 0.343x1 + 1.575x2 + 0.001x3 + 0.895x4 - 5.865x5
Where y= employment status
X1= age
X2= sex
X3=race
X4=marital status
X5=labor force
From the above model, it can be seen that there is a positive correlation between employment status (explained/dependent variable) and for the explanatory variable( age, sex, race, and marital status), and a negative relationship between employment status and the labor force.
The age of the respondents influenced the employment status of an individual in that those in the working-age category were more likely to be working than those outside this category. Individuals in the working-age bracket are 34.2% more likely to be compared to others because employers would not want to hire underage individuals, risk being sued for child labor violations, or employ older individuals, making the productivity of their industry suffer.
Gender and sex of an individual play a critical role in securing employment status; from the data, it can be seen that males are twice (1.575) more likely to be employed compared to their female counterparts. The is because of the gender gap and gender discrimination in workplaces.
Ironically, race's influence in ensur...
Model
Y=β0 + β1x1 + β2x2 + β3x3 + β4x4 + β5x5
Y= 11.037 + 0.343x1 + 1.575x2 + 0.001x3 + 0.895x4 - 5.865x5
Where y= employment status
X1= age
X2= sex
X3=race
X4=marital status
X5=labor force
From the above model, it can be seen that there is a positive correlation between employment status (explained/dependent variable) and for the explanatory variable( age, sex, race, and marital status), and a negative relationship between employment status and the labor force.
The age of the respondents influenced the employment status of an individual in that those in the working-age category were more likely to be working than those outside this category. Individuals in the working-age bracket are 34.2% more likely to be compared to others because employers would not want to hire underage individuals, risk being sued for child labor violations, or employ older individuals, making the productivity of their industry suffer.
Gender and sex of an individual play a critical role in securing employment status; from the data, it can be seen that males are twice (1.575) more likely to be employed compared to their female counterparts. The is because of the gender gap and gender discrimination in workplaces.
Ironically, race's influence in ensur...
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